It is no secret that AI continues to dominate the discussion about innovation across industries. The race for organizations to get on the AI train is heating up, but many still don’t fully understand what types of solutions might be the best fit for them. Even in today’s business environment where “if you’re not first, you’re last,” it’s crucial for organizations to understand the different AI types for enterprise and how to best (and responsibly) implement them into daily workflow. In this news roundup, we explore how the various AI types for enterprise compare and where they’re making the largest impact.
AI types for Enterprise:
Understanding the different types of artificial intelligence – IBM
Early iterations of the AI applications we interact with most today were built on traditional machine learning models. These models rely on learning algorithms that are developed and maintained by data scientists. In other words, traditional machine learning models need human intervention to process new information and perform any new task that falls outside their initial training.
Due to deep learning and other advancements, the field of AI remains in a constant and fast-paced state of flux. Our collective understanding of realized AI and theoretical AI continues to shift, meaning AI categories and AI terminology may differ (and overlap) from one source to the next. However, the types of AI can be largely understood by examining two encompassing categories: AI capabilities and AI functionalities.
8 Types of AI You Should Know About in 2025 – Net Guru
Artificial intelligence exists in many specialized forms rather than as the human-like robots often depicted in science fiction. Through our exploration of eight distinct AI types, we see how each serves unique purposes – from Narrow AI handling specific tasks to Expert Systems making complex domain-specific decisions.
Though each type has limitations, their combined capabilities create powerful solutions. Narrow AI excels at specialized tasks, Generative AI creates new content, Predictive AI forecasts outcomes, while Expert Systems apply deep domain knowledge. Limited Memory AI learns from experience, Computer Vision interprets visual data, and NLP bridges human-machine communication gaps.
AI Is Evolving — And Changing Our Understanding Of Intelligence – NOEMA
The emergence of artificial life looks like a phase transition, as when water freezes or boils. But whereas conventional phases of matter are characterized by their statistical uniformity — an ordered atomic lattice for ice, random atomic positions for gas and somewhere in between for liquid — living matter is vastly more complex, exhibiting varied and purposeful structure at every scale. This is because computation requires distinct functional parts that must work together, as evident in any machine, organism or program.
There’s something magical about watching complex, purposeful and functional structures emerging out of random noise in our simulations. But there is nothing supernatural or miraculous about it. Similar phase transitions from non-life to life occurred on Earth billions of years ago, and we can hypothesize similar events taking place on other life-friendly planets or moons.
This computational view of life also offers insight into life’s increasing complexity over evolutionary time. Because computational matter — including life itself — is made out of distinct parts that must work together, evolution operates simultaneously on the parts and on the whole, a process known in biology as “multilevel selection.”
Narrow AI:
Narrow vs. General AI: Key Differences and Finance Applications – Corporate Finance Institute
Narrow AI, sometimes called “weak AI,” refers to systems designed and trained to perform specific tasks. These systems are highly specialized and excel within their defined scope, but they lack the ability to operate outside of it.
General AI, also referred to as “strong AI,” represents the theoretical pinnacle of artificial intelligence. Unlike Narrow AI, General AI would have the ability to perform any intellectual task that a human can do, adapting to new challenges and applying knowledge across diverse domains.
If General AI is the ultimate goal, why is Narrow AI so prevalent? Simply put, Narrow AI works. Its focused nature makes it easier to develop, deploy, and refine. This immediate application to critical functions is why financial institutions and corporations have embraced it.
OpenAI Unveils Agent That Can Make Spreadsheets and PowerPoints – WSJ
The difference between Operator and ChatGPT agent, however, is that the new agent is equipped with “deep research” capabilities that allow it to synthesize larger amounts of information it gathers from the web. That makes ChatGPT agent a combination of Operator and OpenAI’s deep research agent, the company said.
AI agents are generally known as bots that can perform tasks on behalf of humans. They promise to usher in a generation of products and capabilities that will drive revenue and lower cost for businesses. But they haven’t yet delivered on that goal for enterprises, and need to connect to other apps and services to be truly useful.
Generative & Super AI:
Generative AI vs Predictive AI: Exploring Creativity and Analysis – eWeek
Generative AI focuses on creativity, using sophisticated modeling techniques to produce original content. It’s about generating text, images, videos, and even software code based on user input, making it a valuable tool for creative applications.
Generative AI is an evolving technology that creates content using artificial intelligence. You may already be familiar with popular GenAI tools like ChatGPT, Midjourney, and Runway. Millions of users now use these programs to create text, images, video, music, and software code.
At its core, generative AI relies on advanced algorithms, deep learning, and neural network techniques to produce content. Analyzing massive datasets, it identifies patterns in language, imagery, and structure, allowing it to mimic various styles.
5 Ways to Stay Smart When Using Gen AI, Explained by Computer Science Professors – CNET
This week, the Austin, Texas, conference has spotlighted artificial intelligence. Experts discussed the future and the big picture, with talks on trust, the changing workplace and more. CMU assistant professors Sherry Wu and Maarten Sap focused more on the here and now, with some tips on how best to use, and not misuse, the most common generative AI tools out there, like AI chatbots trained on large language models.
Perhaps the biggest issue with generative AI tools is that they hallucinate, meaning they make stuff up. Sap said hallucinations can happen up to a quarter of the time, with higher rates in more specialized areas like law and medicine.
The problem goes beyond just getting things wrong. Sap said chatbots can appear confident in an answer while being completely wrong.
Traditional AI vs. Generative AI: A Breakdown – US Chamber of Commerce
Traditional AI is a subset of artificial intelligence that focuses on performing preset tasks using predetermined algorithms and rules. These AI applications are designed to excel in a single activity or a restricted set of tasks, such as playing chess, diagnosing diseases, or translating languages.
There are plenty of useful applications for traditional AI. Some artificial intelligence spam filters, for instance, use predefined rules to isolate spam emails from your main inbox. Ultimately, however, traditional AI is only as effective as the data used to train the algorithm. It has limited efficacy in streamlining and optimizing your business.
Generative AI’s Unprecedented Adoption Cycle – Forbes
Few technologies have moved as swiftly from the margins to the mainstream as generative AI. Just two and a half years ago, OpenAI’s ChatGPT burst onto the scene, triggering a seismic shift that catapulted generative AI onto the global stage and made it an everyday companion for hundreds of millions of users. It is no understatement to say that we are witnessing one of the fastest adoption cycles in tech history—a pace reminiscent of the early days of the personal computer revolution.
Yet, despite all the extraordinary advances since 2023, the real story lies ahead. The market for generative AI is ripe for unprecedented growth. Recent projections place its global value at a staggering $356 billion by 2030—a leap of 10x from where we are today. This scale of expansion is poised to reshape the broader tech landscape fundamentally.
Rethinking public administration in the age of generative AI – Federal News Network
The age of generative AI is reshaping expectations across every sector, including government. As these tools move from novelty to necessity, public agencies are facing a clear mandate: evolve or fall behind. The rise of AI tools such as ChatGPT, now well established far beyond the technology sector, will increase the pressure to innovate in many areas. From intelligent chatbots and document summarization to predictive analytics and automated workflows, AI is changing the way people expect information to be delivered and decisions to be made. Still, agencies must carefully evaluate tools through the lens of public-sector requirements, prioritizing platforms that enable transparency, security and control. Robust governance capabilities aren’t optional — they’re foundational to ensuring long-term trust and operational resilience.
Agentic AI:
Trust and human oversight to define the next era of agentic AI, study finds – FutureCIO
Trust in fully autonomous AI agents has declined significantly, dropping from 43% to 27% over the past year. Almost two in five executives believe that the risks of implementing AI agents outweigh the benefits. Only 40% of organisations say they trust AI agents to manage tasks and processes autonomously, while most do not fully trust the technology.
A.I. Isn’t Magic, but Can It Be ‘Agentic’? – The New York Times
It is a fancified way to say something acts like an agent. Unlike chatbots, which require a human to type in a prompt before it can spit out a response, agentic A.I. can act on its own. A customer could create a complex goal, like predicting which factory machines will need maintenance or booking a trip, and the A.I. would automatically complete the required tasks.
Or at least, that’s the idea. Most agentic A.I. is still in the “possibility” stage. And that means it’s a great time for tech companies to promote the heck out of it.
Are Agentic AI Systems Quietly Taking Over Enterprises? 3 Ways To Keep Humans In The Loop – Forbes
Imagine a future where AI agents run the majority of your company’s daily operations by handling complex tasks, managing workflows, and resolving customer issues around the clock, all while reporting to another AI agent manager who then reports to you. Picture reaching out to McKinsey and instead of a human consultant, being connected with a customized AI agent that provides expert insights instantly. That future is nearly here. Agentic AI is rapidly reshaping how enterprises operate. At Salesforce, these AI agents now manage 30 to 50 percent of internal workflows, and more than 85 percent of customer service inquiries are resolved by AI, dramatically easing the burden on human staff. CEO Marc Benioff, known for his bold branding, has even called himself the “Taylor Swift of Tech,” comparing Salesforce’s AI transformation to the sweeping impact of Swift’s multi-era world tours.
With AI vastly emerging, which ones will shape your industry the most?
Do you have innovative AI solutions or authoritative thought leadership, but unsure how to present yourself to the media? FischTank PR is committed to building custom messaging and strategies for organizations innovating with AI. As a top B2B and innovation tech PR firm covering GenAI, AI/ML, enterprise tech and more, we help forward-thinking companies earn meaningful exposure, advance key narratives and build brand awareness.
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***News roundup guest post from FischTank PR interns Abby Collins and Laura Gruener***
